1. Field of the Invention
This invention relates to non-invasive means and method for determining the size of a lesion in lung cancer patients. The current state of the art allows doctors to determine changes in cancerous lesion size only through the use of unaided radiological observation of longitudinal CT exams. Determination of lesion size, or the change in lesion size, is important in the determination of the success of lung cancer treatment. The use of a computer program implementing statistical analysis of CT scans from different timeframes provides for significantly enhanced detection of changes in lesion size.
2. Background of the Invention
The current state of knowledge is as follows.
Accurate assessment of lesion size change in longitudinal high resolution CT scans of the lung is a critical component of lung cancer differential diagnosis as well as lung cancer therapy assessment. The standard method for assessing lesion size change has traditionally relied upon unaided radiological observation of longitudinal CT exams. Even when quantification assistance is provided through the use of electronic calipers operating across one or two dimensions to measure lesion diameter(s), these methods have been shown to yield high inter- and intra-observer misclassification rates[1]. Follow-up CT is commonly used to determine if lung nodule biopsy should be considered, thus a significant improvement in the accuracy and precision of lesion size change has the potential to reduce the unnecessary biopsy rate and reduce the amount of time needed for a meaningful follow-up CT scan. In the context of drug therapy assessment, the impact of an improved metric over the current RECIST criteria[2] could significantly reduce the time and number of patients needed to evaluate drug efficacy.
Significant and sustained improvements in CT acquisition technology over the last decade have made large coverage acquisition of sub-millimeter isotropic resolution CT scans of the lung commonly available. The wide-spread availability of this acquisition technology has led many researchers to explore the potential of volumetric lesion segmentation algorithms in order to characterize lesion change[3-6]. The main advantage of volumetric assessment over uni-dimensional or bi-dimensional lesion diameter measurement resides in the ability to fully measure change over three dimensions. Another important advantage of volumetric segmentation is the ability to perform the measurement objectively, reducing the subjective biases that commonly influence human observers. Although research in this field has shown significant promise, it is widely recognized that the methods developed to date are easily confounded by typical sources of patient and image quality variance found during routine clinical care. Performance of these methods has been shown to degrade rapidly when the full variation of lesion presentations is provided and when acquisition parameters are not strictly controlled. As a result, the research methods and commercial products developed to date have been limited to research use by clinical experts in quantitative imaging. The development of an accurate and precise quantitative method that is robust to common sources of patient and acquisition device variance is a necessary requirement before quantitative assessment methodology can be deployed widely and impact the larger patient population.
An improved change assessment algorithm is needed that is capable of robustly handling the full variance in CT lesion presentation as well as the many different acquisition devices and protocols possible. In particular, a volumetric assessment method that can support a wide range of variation in acquisition devices and acquisition parameters, even along the course of a single patient's longitudinal study, would be beneficial in current clinical care setting as well as in drug clinical trials. Although no measurement method can make up for poor acquisition data, a method can be optimized to obtain the most information from the data available and to provide the user with guidance on the confidence with which a classification has been measured.